Vision-based Advanced Driver Assistance Systems (ADAS) assist drivers during the driving process to increase vehicle and road safety. Some examples of ADAS can include, but are not limited to, in-vehicle navigation systems, Adaptive Cruise Control (ACC) systems, lane departure warning (LDW) systems, collision avoidance systems, automatic parking systems, and blind spot indicator (BSI) systems.
Modern ADAS rely on computer vision based pedestrian detection for accident prevention. Sensors can be equipped in vehicles to collect data from the vehicle surroundings and decision can be made based on sensory data. Sensors for detecting pedestrians can be cameras that capture images of vehicle surroundings (e.g., a driving scene). In these images, pedestrians can be partially occluded by objects, such as cars, trees, shrubbery, signs, among others. Determining whether a region in a driving scene belongs to a target object or an occluded object facilitates ADAS and can help save lives by preventing fatal accidents with pedestrians. Accordingly, ADAS can use computer vision techniques, for example, techniques based on a deformable parts model (DPM) to detect partially occluded pedestrians.